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Today I attended to the first day of the 5th TCE conference, this year topic was “Scaling Systems for Big Data“. There were some nice lectures, especially the first one which was the best of today.

This lecture was from the Software Reliability Lab a research group in the Department of Computer Science at ETH Zurich led by Prof. Martin Vechev, who presented the lecture. The topic was “Machine Learning for Programming” where machine learning is used on open source repositories (github and alike) to create statistical models for things that were once “science fiction” like – code completion (not a single word or method but full bunch of code into a method), de-obfuscation (given an obfuscated code you’ll get a nicely un-obfuscated code with meaningful variable names and type) and others…. This is a very interesting usage of machine learning and perhaps soon we (developers) may be obsolete 🙂
Some tools using this technique – http://jsnice.org which shows de-obfuscation of javascript code and the http://nice2predict.org framework on top is built jsnice.

Few facts from a short google talk on building scalable cloud storage:

The corpus size is growing exponentially (nothing really new here)

Systems (“cloud storage systems”) require a major redesign every 5 years. That’s the interesting fact… Let remember Google had GFS (Google file system – which HDFS is an implementation of it), then Google moved to Colossus (in 2012) so according to that in 2017 should we see a new file system? If so they certainly work on it already….

If you are interested in mining and checking MS Excel files for error and suspicious values (indicating that some values might be human error) then checkcell.org might be the solution for you. What about survey? Can survey have errors too? Well it seems that same question presenting in different order will produce different results (human are sometimes really non logical) so if you have a survey and want to check if you inserted some bias by mistake then surveyman is the answer. You can refer to Emery Berger’s (who gave the talk) blogs for cellckeck and surveyman (http://emeryberger.com/research/checkcell/ and http://emeryberger.com/research/surveyman/ respectively)

Another nice talk from Lorenzo Avisi (UT Austin) about SALT. A combination between the ACID and BASE (in chemistry ACID + BASE = SALT) principle in a distributed database. So you can scale a system and still use relational database concept instead of moving to a pure BASE databases which increase the system complexity. The idea is to break relational transactions into new transaction types having better granularity and scalability. The full paper can be found here https://www.cs.utexas.edu/lasr/paper.php?uid=84

By the way if you are using map reduce an interesting fact from another talk by Bianca Schroeder from Toronto University (this is a starting paper is) that long running jobs tend to fail more often that short ones and retrying the execution more than twice is just a waste of cluster resource because it will almost for sure fail again. By using machine learning the research team is able to predict after 5 minutes of run the probability of failure of the job or not. The observation were done on google cluster and open cluster too. This is for sure a nice future paper…